Covid19, Japan

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数^*^	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

^*^ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 60787

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-07T21:51:43+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	109128
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	107440	0
dateAnnounced	0	1.00	10	10	0	284	0
gender	17026	0.84	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	105144	0.04	8	23	0	8	0
notes	56618	0.48	1	270	0	49741	1
mhlwPatientNumber	108679	0.00	1	11	0	434	0
prefecturePatientNumber	14727	0.87	5	20	0	94392	0
prefectureSourceURL	77824	0.29	5	224	0	3439	0
residence	25002	0.77	1	38	0	1422	0
sourceURL	637	0.99	1	239	0	8134	0
relatedPatients	98525	0.10	2	259	0	6433	0
knownCluster	106646	0.02	3	88	0	229	0
detectedCityTown	82871	0.24	2	22	0	663	0
cityPrefectureNumber	83137	0.24	1	34	0	25982	2
citySourceURL	97296	0.11	9	317	0	3637	0
deceasedDate	107303	0.02	10	10	0	234	0
deceasedReportedDate	107913	0.01	10	62	0	205	0
deathSourceURL	108058	0.01	14	123	0	651	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.98	TRU: 107439, FAL: 1689
charterFlightPassenger	109114	0	1.00	TRU: 14
cruisePassengerDisembarked	109117	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.88	23.82	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	107439
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	107439	0
pref	0	1.00	3	15	0	49	0
knownCluster	104986	0.02	3	88	0	227	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-07	2020-08-14	284
deceasedDate	107060	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	107109	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	16360	0.85	FALSE	2	M: 51150, F: 39929
patientStatus	104906	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 24981, -1: 16458, 30: 15816, 40: 13112
pcode	1259	0.99	FALSE	47	13: 32459, 27: 13676, 14: 9275, 23: 6738
Pref	1259	0.99	FALSE	47	東京都: 32459, 大阪府: 13676, 神奈川: 9275, 愛知県: 6738
region	1259	0.99	FALSE	8	関東地: 55476, 近畿地: 21512, 九州地: 11178, 中部地: 10494
広域圏	8607	0.92	FALSE	8	首都圏: 55709, 近畿圏: 20918, 中部圏: 9166, 九州圏: 7687
通俗的区分	1259	0.99	FALSE	11	関東: 55476, 関西: 20918, 東海: 8798, 九州: 7687
fct_pref	1259	0.99	FALSE	47	Tok: 32459, Osa: 13676, Kan: 9275, Aic: 6738

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 107439
charterFlightPassenger	107425	0	1.00	TRU: 14
cruisePassengerDisembarked	107428	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 104986, TRU: 2453

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地域別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 10)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 5

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 157.2492 178.5668 167.2026 174.9449 196.5598 214.1653 243.4206 252.1247
##  [9] 261.0719 258.8653 263.0759 274.1507 292.9702 315.7581
## 
## $北海道地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 148.0745 143.2176
## 43.71429 168.5144 163.1930
## 43.85714 154.6340 147.9806
## 44.00000 161.3335 154.1280
## 44.14286 181.7127 173.8532
## 44.28571 197.5805 188.8010
## 44.42857 224.8112 214.9600
## 44.57143 230.4219 218.9332
## 44.71429 236.4879 223.4739
## 44.85714 231.2900 216.6925
## 45.00000 233.0447 217.1471
## 45.14286 241.7972 224.6703
## 45.28571 258.2607 239.8866
## 45.42857 278.3746 258.5849
## 
## $北海道地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 166.4240 171.2809
## 43.71429 188.6192 193.9406
## 43.85714 179.7712 186.4246
## 44.00000 188.5563 195.7617
## 44.14286 211.4068 219.2663
## 44.28571 230.7501 239.5296
## 44.42857 262.0300 271.8813
## 44.57143 273.8274 285.3161
## 44.71429 285.6558 298.6698
## 44.85714 286.4407 301.0382
## 45.00000 293.1071 309.0046
## 45.14286 306.5042 323.6311
## 45.28571 327.6797 346.0538
## 45.42857 353.1417 372.9314
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 24.60419 30.08920 21.62496 26.20859 29.46347 26.74773 24.85472 27.44988
##  [9] 24.56149 26.87458 26.73813 25.28904 27.46939 27.32437
## 
## $東北地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 18.13152 14.70509
## 43.71429 23.45136 19.93750
## 43.85714 14.82597 11.22680
## 44.00000 19.25218 15.56968
## 44.14286 22.35312 18.58913
## 44.28571 19.48671 15.64296
## 44.42857 17.44609 13.52420
## 44.57143 19.63959 15.50508
## 44.71429 16.56853 12.33731
## 44.85714 18.70303 14.37727
## 45.00000 18.39181 13.97354
## 45.14286 16.77154 12.26264
## 45.28571 18.78408 14.18635
## 45.42857 18.47443 13.78955
## 
## $東北地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 31.07687 34.50330
## 43.71429 36.72704 40.24090
## 43.85714 28.42396 32.02312
## 44.00000 33.16500 36.84750
## 44.14286 36.57382 40.33781
## 44.28571 34.00875 37.85250
## 44.42857 32.26335 36.18524
## 44.57143 35.26016 39.39468
## 44.71429 32.55445 36.78566
## 44.85714 35.04613 39.37188
## 45.00000 35.08445 39.50272
## 45.14286 33.80654 38.31544
## 45.28571 36.15471 40.75243
## 45.42857 36.17432 40.85919
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 380.3784 261.5940 380.6297 407.4204 525.3681 542.5199 555.8603 404.2329
##  [9] 278.9248 411.1505 409.5703 527.4692 526.9877 553.1839
## 
## $関東地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 318.0129 284.99854
## 43.71429 183.8498 142.69456
## 43.85714 295.4329 250.33245
## 44.00000 319.8721 273.52688
## 44.14286 435.2956 387.61417
## 44.28571 449.5429 400.32380
## 44.42857 457.4444 405.34623
## 44.57143 294.5679 236.51470
## 44.71429 160.0458  97.11499
## 44.85714 286.5830 220.64096
## 45.00000 281.3359 213.45275
## 45.14286 396.6073 327.33318
## 45.28571 393.3586 322.61954
## 45.42857 416.4079 344.00307
## 
## $関東地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 442.7440 475.7583
## 43.71429 339.3381 380.4934
## 43.85714 465.8264 510.9269
## 44.00000 494.9687 541.3140
## 44.14286 615.4406 663.1220
## 44.28571 635.4969 684.7160
## 44.42857 654.2762 706.3744
## 44.57143 513.8980 571.9512
## 44.71429 397.8038 460.7346
## 44.85714 535.7180 601.6600
## 45.00000 537.8046 605.6878
## 45.14286 658.3311 727.6052
## 45.28571 660.6169 731.3559
## 45.42857 689.9599 762.3648
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 173.0160 153.6890 186.1884 177.1670 181.6947 195.7503 216.8805 200.9347
##  [9] 206.6588 207.2905 208.5507 212.4333 217.0238 242.9510
## 
## $中部地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 155.4001 146.0749
## 43.71429 130.9311 118.8838
## 43.85714 158.3821 143.6623
## 44.00000 144.7106 127.5291
## 44.14286 146.2764 127.5271
## 44.28571 156.4086 135.5823
## 44.42857 174.6640 152.3160
## 44.57143 153.4753 128.3518
## 44.71429 154.1642 126.3753
## 44.85714 150.9722 121.1591
## 45.00000 147.8648 115.7396
## 45.14286 148.0991 114.0427
## 45.28571 149.3261 113.4891
## 45.42857 171.5649 133.7754
## 
## $中部地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 190.6318 199.9571
## 43.71429 176.4468 188.4941
## 43.85714 213.9947 228.7144
## 44.00000 209.6235 226.8049
## 44.14286 217.1130 235.8623
## 44.28571 235.0921 255.9184
## 44.42857 259.0970 281.4450
## 44.57143 248.3941 273.5176
## 44.71429 259.1533 286.9422
## 44.85714 263.6088 293.4219
## 45.00000 269.2366 301.3617
## 45.14286 276.7675 310.8240
## 45.28571 284.7215 320.5585
## 45.42857 314.3371 352.1267
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 236.8032 174.8210 276.1458 219.1361 261.6986 295.0684 290.5848 254.5405
##  [9] 204.6095 283.9587 249.2862 273.0501 296.9028 290.9629
## 
## $近畿地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 205.7841 189.3637
## 43.71429 139.0459 120.1077
## 43.85714 238.1930 218.1020
## 44.00000 178.0265 156.2643
## 44.14286 218.6452 195.8541
## 44.28571 249.6830 225.6574
## 44.42857 243.4971 218.5703
## 44.57143 198.6230 169.0221
## 44.71429 144.5837 112.8080
## 44.85714 220.9138 187.5398
## 45.00000 183.0817 148.0351
## 45.14286 204.2880 167.8876
## 45.28571 225.5961 187.8486
## 45.42857 217.4753 178.5733
## 
## $近畿地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 267.8222 284.2426
## 43.71429 210.5961 229.5343
## 43.85714 314.0987 334.1897
## 44.00000 260.2457 282.0079
## 44.14286 304.7520 327.5432
## 44.28571 340.4538 364.4794
## 44.42857 337.6725 362.5993
## 44.57143 310.4579 340.0588
## 44.71429 264.6353 296.4110
## 44.85714 347.0036 380.3775
## 45.00000 315.4907 350.5372
## 45.14286 341.8121 378.2126
## 45.28571 368.2096 405.9571
## 45.42857 364.4505 403.3524
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 12.52617 12.36643 12.36643 12.36643 12.36643 12.36643 12.36643 12.36643
##  [9] 12.36643 12.36643 12.36643 12.36643 12.36643 12.36643
## 
## $中国地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 3.4900548 -1.293375
## 43.71429 2.9227916 -2.076368
## 43.85714 2.7340553 -2.365016
## 44.00000 2.5489468 -2.648115
## 44.14286 2.3672644 -2.925974
## 44.28571 2.1888247 -3.198874
## 44.42857 2.0134601 -3.467071
## 44.57143 1.8410169 -3.730800
## 44.71429 1.6713537 -3.990278
## 44.85714 1.5043403 -4.245702
## 45.00000 1.3398563 -4.497259
## 45.14286 1.1777900 -4.745118
## 45.28571 1.0180381 -4.989438
## 45.42857 0.8605039 -5.230365
## 
## $中国地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 21.56228 26.34571
## 43.71429 21.81007 26.80923
## 43.85714 21.99880 27.09788
## 44.00000 22.18391 27.38097
## 44.14286 22.36560 27.65883
## 44.28571 22.54403 27.93173
## 44.42857 22.71940 28.19993
## 44.57143 22.89184 28.46366
## 44.71429 23.06151 28.72314
## 44.85714 23.22852 28.97856
## 45.00000 23.39300 29.23012
## 45.14286 23.55507 29.47798
## 45.28571 23.71482 29.72230
## 45.42857 23.87236 29.96322
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.9391447 1.1009978 1.1009978 1.1009978 1.1009978 1.1009978 1.1009978
##  [8] 1.1009978 1.1009978 1.1009978 1.1009978 1.1009978 1.1009978 1.1009978
## 
## $四国地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -2.549712 -4.396602
## 43.71429 -2.644981 -4.627983
## 43.85714 -2.715976 -4.736560
## 44.00000 -2.785674 -4.843154
## 44.14286 -2.854144 -4.947870
## 44.28571 -2.921449 -5.050804
## 44.42857 -2.987646 -5.152044
## 44.57143 -3.052789 -5.251670
## 44.71429 -3.116925 -5.349758
## 44.85714 -3.180100 -5.446377
## 45.00000 -3.242357 -5.541590
## 45.14286 -3.303734 -5.635458
## 45.28571 -3.364267 -5.728036
## 45.42857 -3.423991 -5.819375
## 
## $四国地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 4.428002 6.274891
## 43.71429 4.846977 6.829978
## 43.85714 4.917972 6.938555
## 44.00000 4.987670 7.045150
## 44.14286 5.056140 7.149866
## 44.28571 5.123445 7.252800
## 44.42857 5.189642 7.354039
## 44.57143 5.254784 7.453666
## 44.71429 5.318920 7.551754
## 44.85714 5.382096 7.648373
## 45.00000 5.444353 7.743586
## 45.14286 5.505730 7.837454
## 45.28571 5.566263 7.930032
## 45.42857 5.625987 8.021371
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 45.55199 41.68763 53.63851 51.90734 56.36873 54.99568 52.45299 51.46401
##  [9] 48.32666 55.63860 53.37610 57.79988 57.59219 54.29744
## 
## $九州地方$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%          95%
## 43.57143 23.2020448  11.37069894
## 43.71429 14.1430272  -0.43820499
## 43.85714 23.1815528   7.05861472
## 44.00000 20.4428898   3.78661345
## 44.14286 22.9725398   5.29366395
## 44.28571 18.9886150  -0.07237558
## 44.42857 14.2168327  -6.02416433
## 44.57143  8.0328594 -14.95820510
## 44.71429  1.8168424 -22.80396622
## 44.85714  6.0066048 -20.26698462
## 45.00000  1.6832095 -25.68135139
## 45.14286  3.3559917 -25.46486168
## 45.28571  0.7793171 -29.29560318
## 45.42857 -5.0959700 -36.53694142
## 
## $九州地方$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143  67.90193  79.73328
## 43.71429  69.23223  83.81347
## 43.85714  84.09546 100.21840
## 44.00000  83.37180 100.02808
## 44.14286  89.76492 107.44380
## 44.28571  91.00275 110.06374
## 44.42857  90.68914 110.93014
## 44.57143  94.89517 117.88623
## 44.71429  94.83647 119.45728
## 44.85714 105.27059 131.54418
## 45.00000 105.06898 132.43354
## 45.14286 112.24377 141.06462
## 45.28571 114.40507 144.47999
## 45.42857 113.69085 145.13182

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 157.2492 178.5668 167.2026 174.9449 196.5598 214.1653 243.4206 252.1247
##  [9] 261.0719 258.8653 263.0759 274.1507 292.9702 315.7581
## 
## $北海道$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 148.0745 143.2176
## 43.71429 168.5144 163.1930
## 43.85714 154.6340 147.9806
## 44.00000 161.3335 154.1280
## 44.14286 181.7127 173.8532
## 44.28571 197.5805 188.8010
## 44.42857 224.8112 214.9600
## 44.57143 230.4219 218.9332
## 44.71429 236.4879 223.4739
## 44.85714 231.2900 216.6925
## 45.00000 233.0447 217.1471
## 45.14286 241.7972 224.6703
## 45.28571 258.2607 239.8866
## 45.42857 278.3746 258.5849
## 
## $北海道$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 166.4240 171.2809
## 43.71429 188.6192 193.9406
## 43.85714 179.7712 186.4246
## 44.00000 188.5563 195.7617
## 44.14286 211.4068 219.2663
## 44.28571 230.7501 239.5296
## 44.42857 262.0300 271.8813
## 44.57143 273.8274 285.3161
## 44.71429 285.6558 298.6698
## 44.85714 286.4407 301.0382
## 45.00000 293.1071 309.0046
## 45.14286 306.5042 323.6311
## 45.28571 327.6797 346.0538
## 45.42857 353.1417 372.9314
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 2.026616 6.330863 3.249301 5.455500 3.876004 5.006821 4.197230 4.776845
##  [9] 4.361878 4.658967 4.446270 4.598547 4.489527 4.567579
## 
## $青森県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%        95%
## 43.57143 -0.5065585 -1.8475399
## 43.71429  3.3815427  1.8202666
## 43.85714  0.2444125 -1.3462794
## 44.00000  2.2029826  0.4812039
## 44.14286  0.5346386 -1.2341736
## 44.28571  1.4863433 -0.3772850
## 44.42857  0.5725409 -1.3462535
## 44.57143  1.0062322 -0.9898093
## 44.71429  0.4823339 -1.5713726
## 44.85714  0.6518535 -1.4693843
## 45.00000  0.3307239 -1.8479149
## 45.14286  0.3668373 -1.8732948
## 45.28571  0.1521373 -2.1439382
## 45.42857  0.1218869 -2.2315202
## 
## $青森県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 4.559790  5.900771
## 43.71429 9.280184 10.841460
## 43.85714 6.254189  7.844881
## 44.00000 8.708017 10.429796
## 44.14286 7.217370  8.986183
## 44.28571 8.527298 10.390927
## 44.42857 7.821918  9.740713
## 44.57143 8.547457 10.543498
## 44.71429 8.241422 10.295128
## 44.85714 8.666081 10.787318
## 45.00000 8.561817 10.740456
## 45.14286 8.830258 11.070390
## 45.28571 8.826917 11.122992
## 45.42857 9.013270 11.366677
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.24152704 0.22623849 0.69636017 0.22623744 0.19502169 0.54219567
##  [7] 0.21952721 0.28692567 0.28311698 0.09493048 0.28311720 0.28927788
## [13] 0.17022186 0.28451228
## 
## $岩手県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%        95%
## 43.57143 -0.34456033 -0.6548162
## 43.71429 -0.36012288 -0.6705238
## 43.85714  0.10875492 -0.2023045
## 44.00000 -0.36260905 -0.6743255
## 44.14286 -0.39506343 -0.7074356
## 44.28571 -0.04912548 -0.3621520
## 44.42857 -0.37302740 -0.6867069
## 44.57143 -0.33400424 -0.6627047
## 44.71429 -0.33872715 -0.6679115
## 44.85714 -0.52874967 -0.8589060
## 45.00000 -0.34239358 -0.6735190
## 45.14286 -0.33805819 -0.6701498
## 45.28571 -0.45893420 -0.7919893
## 45.42857 -0.34645853 -0.6804743
## 
## $岩手県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143 0.8276144 1.137870
## 43.71429 0.8125999 1.123001
## 43.85714 1.2839654 1.595025
## 44.00000 0.8150839 1.126800
## 44.14286 0.7851068 1.097479
## 44.28571 1.1335168 1.446543
## 44.42857 0.8120818 1.125761
## 44.57143 0.9078556 1.236556
## 44.71429 0.9049611 1.234146
## 44.85714 0.7186106 1.048767
## 45.00000 0.9086280 1.239753
## 45.14286 0.9166139 1.248706
## 45.28571 0.7993779 1.132433
## 45.42857 0.9154831 1.249499
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 16.40507 17.37169 16.62239 17.20323 16.75298 17.10200 16.83145 17.04117
##  [9] 16.87860 17.00462 16.90694 16.98266 16.92396 16.96946
## 
## $宮城県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 11.95814  9.604083
## 43.71429 12.88153 10.504575
## 43.85714 11.73524  9.148134
## 44.00000 12.23103  9.598905
## 44.14286 11.49771  8.715734
## 44.28571 11.73440  8.892960
## 44.42857 11.24048  8.280795
## 44.57143 11.32318  8.296257
## 44.71429 10.97161  7.844640
## 44.85714 10.96426  7.766684
## 45.00000 10.69896  7.412660
## 45.14286 10.63990  7.282244
## 45.28571 10.42786  6.989035
## 45.42857 10.33988  6.830383
## 
## $宮城県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 20.85199 23.20605
## 43.71429 21.86186 24.23881
## 43.85714 21.50955 24.09666
## 44.00000 22.17543 24.80755
## 44.14286 22.00826 24.79023
## 44.28571 22.46960 25.31104
## 44.42857 22.42243 25.38211
## 44.57143 22.75917 25.78609
## 44.71429 22.78560 25.91257
## 44.85714 23.04499 26.24256
## 45.00000 23.11491 26.40121
## 45.14286 23.32542 26.68308
## 45.28571 23.42006 26.85889
## 45.42857 23.59905 27.10854
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.2053081 0.2246726 0.2246726 0.2246726 0.2246726 0.2246726 0.2246726
##  [8] 0.2246726 0.2246726 0.2246726 0.2246726 0.2246726 0.2246726 0.2246726
## 
## $秋田県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.028144 -1.681094
## 43.71429 -1.020312 -1.679368
## 43.85714 -1.020312 -1.679368
## 44.00000 -1.020312 -1.679368
## 44.14286 -1.020312 -1.679368
## 44.28571 -1.020312 -1.679368
## 44.42857 -1.020312 -1.679368
## 44.57143 -1.020312 -1.679368
## 44.71429 -1.020312 -1.679368
## 44.85714 -1.020312 -1.679368
## 45.00000 -1.020312 -1.679368
## 45.14286 -1.020312 -1.679368
## 45.28571 -1.020312 -1.679368
## 45.42857 -1.020312 -1.679368
## 
## $秋田県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.438760 2.091710
## 43.71429 1.469658 2.128713
## 43.85714 1.469658 2.128713
## 44.00000 1.469658 2.128713
## 44.14286 1.469658 2.128713
## 44.28571 1.469658 2.128713
## 44.42857 1.469658 2.128713
## 44.57143 1.469658 2.128713
## 44.71429 1.469658 2.128713
## 44.85714 1.469658 2.128713
## 45.00000 1.469658 2.128713
## 45.14286 1.469658 2.128713
## 45.28571 1.469658 2.128713
## 45.42857 1.469658 2.128713
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] -0.02504272 -0.11849475 -0.02126334 -0.01990881 -0.01414452  0.02507886
##  [7]  0.04899015  0.07957303  0.10658650  0.13291575  0.15819278  0.18211850
## [13]  0.20445851  0.22503835
## 
## $山形県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.9498698 -1.439444
## 43.71429 -1.0582783 -1.555770
## 43.85714 -0.9849629 -1.495115
## 44.00000 -1.0154151 -1.542404
## 44.14286 -1.0473496 -1.594295
## 44.28571 -1.0493959 -1.618189
## 44.42857 -1.0681032 -1.659457
## 44.57143 -1.0430456 -1.637324
## 44.71429 -1.0484169 -1.659839
## 44.85714 -1.0497838 -1.675867
## 45.00000 -1.0476668 -1.686010
## 45.14286 -1.0426637 -1.691024
## 45.28571 -1.0354068 -1.691752
## 45.42857 -1.0265275 -1.689066
## 
## $山形県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143 0.8997844 1.389358
## 43.71429 0.8212888 1.318780
## 43.85714 0.9424362 1.452588
## 44.00000 0.9755974 1.502587
## 44.14286 1.0190606 1.566006
## 44.28571 1.0995536 1.668346
## 44.42857 1.1660835 1.757437
## 44.57143 1.2021917 1.796470
## 44.71429 1.2615899 1.873012
## 44.85714 1.3156153 1.941699
## 45.00000 1.3640523 2.002396
## 45.14286 1.4069007 2.055261
## 45.28571 1.4443238 2.100669
## 45.42857 1.4766042 2.139143
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 2.843602 2.818147 4.685882 2.749863 3.876764 2.936639 3.184086 3.309858
##  [9] 3.363376 4.762061 3.280551 4.273287 3.483768 3.641423
## 
## $福島県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%        95%
## 43.57143  0.31778764 -1.0192979
## 43.71429  0.21640705 -1.1608707
## 43.85714  2.01037130  0.5940414
## 44.00000  0.00256083 -1.4517729
## 44.14286  1.05950017 -0.4318693
## 44.28571  0.05110862 -1.4763989
## 44.42857  0.23186704 -1.3309431
## 44.57143  0.20033869 -1.4457413
## 44.71429  0.17359934 -1.5149665
## 44.85714  1.49399663 -0.2360120
## 45.00000 -0.06396818 -1.8344497
## 45.14286  0.85402198 -0.9560278
## 45.28571 -0.00864406 -1.8574154
## 45.42857  0.07736490 -1.8093335
## 
## $福島県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 5.369417 6.706503
## 43.71429 5.419886 6.797164
## 43.85714 7.361393 8.777723
## 44.00000 5.497165 6.951498
## 44.14286 6.694028 8.185398
## 44.28571 5.822170 7.349677
## 44.42857 6.136304 7.699115
## 44.57143 6.419377 8.065457
## 44.71429 6.553153 8.241719
## 44.85714 8.030125 9.760133
## 45.00000 6.625070 8.395552
## 45.14286 7.692553 9.502603
## 45.28571 6.976180 8.824952
## 45.42857 7.205481 9.092179
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 6.411678 6.516186 6.422492 6.578432 7.219172 6.892292 7.251990 6.887280
##  [9] 6.887280 6.887280 6.887280 6.887280 6.887280 6.887280
## 
## $茨城県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%          95%
## 43.57143 2.999202  1.192746929
## 43.71429 2.997567  1.134922002
## 43.85714 2.800838  0.883649932
## 44.00000 2.856595  0.886373237
## 44.14286 3.399779  1.377913775
## 44.28571 2.977772  0.905550523
## 44.42857 3.244602  1.123218564
## 44.57143 2.696368  0.477833185
## 44.71429 2.589944  0.315072509
## 44.85714 2.486094  0.156246340
## 45.00000 2.384637  0.001082431
## 45.14286 2.285417 -0.150661445
## 45.28571 2.188292 -0.299201936
## 45.42857 2.093134 -0.444733736
## 
## $茨城県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143  9.824153 11.63061
## 43.71429 10.034806 11.89745
## 43.85714 10.044146 11.96133
## 44.00000 10.300270 12.27049
## 44.14286 11.038566 13.06043
## 44.28571 10.806811 12.87903
## 44.42857 11.259378 13.38076
## 44.57143 11.078191 13.29673
## 44.71429 11.184615 13.45949
## 44.85714 11.288466 13.61831
## 45.00000 11.389922 13.77348
## 45.14286 11.489142 13.92522
## 45.28571 11.586267 14.07376
## 45.42857 11.681425 14.21929
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.460700 1.745865 1.867271 1.638267 1.633656 1.835353 1.852660 1.852160
##  [9] 1.856495 1.856775 1.857899 1.858075 1.858376 1.858449
## 
## $栃木県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.486371 -3.046456
## 43.71429 -1.236900 -2.815880
## 43.85714 -1.270152 -2.931003
## 44.00000 -1.523475 -3.197199
## 44.14286 -1.562342 -3.254201
## 44.28571 -1.377283 -3.077949
## 44.42857 -1.377131 -3.086879
## 44.57143 -1.519217 -3.303916
## 44.71429 -1.538555 -3.335786
## 44.85714 -1.576022 -3.393235
## 45.00000 -1.595247 -3.423232
## 45.14286 -1.616915 -3.456463
## 45.28571 -1.634867 -3.484078
## 45.42857 -1.653022 -3.511882
## 
## $栃木県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 4.407770 5.967855
## 43.71429 4.728630 6.307611
## 43.85714 5.004693 6.665544
## 44.00000 4.800008 6.473733
## 44.14286 4.829653 6.521512
## 44.28571 5.047988 6.748655
## 44.42857 5.082451 6.792199
## 44.57143 5.223538 7.008237
## 44.71429 5.251545 7.048776
## 44.85714 5.289572 7.106785
## 45.00000 5.311044 7.139029
## 45.14286 5.333064 7.172613
## 45.28571 5.351619 7.200830
## 45.42857 5.369919 7.228780
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 5.567124 4.814394 5.653573 5.736848 5.796497 5.726212 6.075288 5.951322
##  [9] 6.033837 5.963619 6.097580 6.030523 6.101245 6.053434
## 
## $群馬県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%       95%
## 43.57143  1.07389828 -1.304672
## 43.71429 -0.33319582 -3.058165
## 43.85714  0.30153620 -2.531661
## 44.00000  0.32177102 -2.544797
## 44.14286  0.06188376 -2.973837
## 44.28571 -0.10733545 -3.195429
## 44.42857  0.12927697 -3.018351
## 44.57143 -0.09547186 -3.296451
## 44.71429 -0.14998587 -3.423504
## 44.85714 -0.27071394 -3.570970
## 45.00000 -0.20212487 -3.536987
## 45.14286 -0.31068820 -3.667523
## 45.28571 -0.29485965 -3.680753
## 45.42857 -0.37310651 -3.775111
## 
## $群馬県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143 10.060349 12.43892
## 43.71429  9.961985 12.68695
## 43.85714 11.005610 13.83881
## 44.00000 11.151924 14.01849
## 44.14286 11.531110 14.56683
## 44.28571 11.559760 14.64785
## 44.42857 12.021298 15.16893
## 44.57143 11.998115 15.19909
## 44.71429 12.217660 15.49118
## 44.85714 12.197952 15.49821
## 45.00000 12.397286 15.73215
## 45.14286 12.371735 15.72857
## 45.28571 12.497349 15.88324
## 45.42857 12.479974 15.88198
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 56.95797 57.69237 57.63328 62.21755 58.84656 92.20756 73.11717 70.54904
##  [9] 67.50744 69.02750 70.47395 68.93384 86.48097 77.82135
## 
## $埼玉県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 43.74602 36.75204
## 43.71429 43.88784 36.58017
## 43.85714 43.26060 35.65216
## 44.00000 47.29833 39.40058
## 44.14286 43.40013 35.22328
## 44.28571 76.25133 67.80462
## 44.42857 56.66693 47.95870
## 44.57143 51.79475 41.86683
## 44.71429 47.95854 37.60997
## 44.85714 48.71504 37.96228
## 45.00000 49.42563 38.28331
## 45.14286 47.17451 35.65582
## 45.28571 64.03315 52.14999
## 45.42857 54.70554 42.46876
## 
## $埼玉県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143  70.16992  77.16391
## 43.71429  71.49689  78.80456
## 43.85714  72.00596  79.61440
## 44.00000  77.13677  85.03453
## 44.14286  74.29299  82.46983
## 44.28571 108.16380 116.61051
## 44.42857  89.56741  98.27564
## 44.57143  89.30333  99.23125
## 44.71429  87.05634  97.40491
## 44.85714  89.33995 100.09272
## 45.00000  91.52228 102.66459
## 45.14286  90.69316 102.21186
## 45.28571 108.92878 120.81194
## 45.42857 100.93716 113.17394
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 47.06308 41.13618 45.72720 42.97476 49.28601 44.80340 49.53333 45.87708
##  [9] 44.71260 46.36029 45.22244 48.66034 46.82290 49.07907
## 
## $千葉県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 35.62708 29.57323
## 43.71429 28.43857 21.71686
## 43.85714 32.64465 25.71916
## 44.00000 29.51828 22.39485
## 44.14286 35.46571 28.14968
## 44.28571 30.62861 23.12493
## 44.42857 35.01270 27.32594
## 44.57143 30.33121 22.10173
## 44.71429 28.58056 20.04078
## 44.85714 29.78401 21.00907
## 45.00000 28.21353 19.20956
## 45.14286 31.22953 22.00221
## 45.28571 28.98016 19.53478
## 45.42857 30.83370 21.17518
## 
## $千葉県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 58.49907 64.55292
## 43.71429 53.83378 60.55549
## 43.85714 58.80975 65.73523
## 44.00000 56.43125 63.55468
## 44.14286 63.10632 70.42235
## 44.28571 58.97820 66.48188
## 44.42857 64.05395 71.74071
## 44.57143 61.42296 69.65244
## 44.71429 60.84464 69.38442
## 44.85714 62.93656 71.71151
## 45.00000 62.23135 71.23532
## 45.14286 66.09115 75.31846
## 45.28571 64.66564 74.11101
## 45.42857 67.32444 76.98295
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 174.5045 125.3770 213.2666 182.8635 274.6775 245.7889 278.8419 174.5518
##  [9] 130.3455 218.0640 187.4956 279.1500 250.1074 283.0117
## 
## $東京都$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 129.33188 105.41895
## 43.71429  73.85496  46.58087
## 43.85714 159.12491 130.46403
## 44.00000 126.38879  96.49291
## 44.14286 216.11141 185.10841
## 44.28571 185.33814 153.33746
## 44.42857 216.68559 183.78201
## 44.57143 105.65878  69.18900
## 44.71429  57.98805  19.68435
## 44.85714 143.29949 103.72157
## 45.00000 110.55482  69.82483
## 45.14286 200.23435 158.45890
## 45.28571 169.39399 126.66690
## 45.42857 200.65765 157.06205
## 
## $東京都$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 219.6771 243.5900
## 43.71429 176.8989 204.1730
## 43.85714 267.4083 296.0692
## 44.00000 239.3381 269.2340
## 44.14286 333.2435 364.2465
## 44.28571 306.2396 338.2403
## 44.42857 340.9983 373.9018
## 44.57143 243.4449 279.9147
## 44.71429 202.7029 241.0065
## 44.85714 292.8284 332.4063
## 45.00000 264.4364 305.1663
## 45.14286 358.0657 399.8412
## 45.28571 330.8208 373.5479
## 45.42857 365.3658 408.9614
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 102.13441  65.82396  84.36548  69.48254 101.80571  98.92792 115.63372
##  [8]  97.79775  79.25528  88.70600  81.11086  97.59844  96.12995 104.65177
## 
## $神奈川県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 82.98340 72.84547
## 43.71429 44.54282 33.27726
## 43.85714 62.40701 50.78289
## 44.00000 47.18121 35.37560
## 44.14286 79.26137 67.32711
## 44.28571 76.17574 64.13146
## 44.42857 92.68760 80.54066
## 44.57143 71.56640 57.68037
## 44.71429 51.92801 37.46183
## 44.85714 60.79419 46.01857
## 45.00000 52.77104 37.76885
## 45.14286 68.88729 53.68852
## 45.28571 67.07082 51.68785
## 45.42857 75.25587 59.69463
## 
## $神奈川県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 121.28541 131.42334
## 43.71429  87.10511  98.37067
## 43.85714 106.32395 117.94806
## 44.00000  91.78388 103.58949
## 44.14286 124.35006 136.28431
## 44.28571 121.68011 133.72439
## 44.42857 138.57985 150.72679
## 44.57143 124.02909 137.91513
## 44.71429 106.58255 121.04874
## 44.85714 116.61781 131.39343
## 45.00000 109.45067 124.45287
## 45.14286 126.30960 141.50836
## 45.28571 125.18908 140.57205
## 45.42857 134.04767 149.60892
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.9273938 0.8811623 0.8193651 0.7689370 1.0328462 0.8602525 0.9762075
##  [8] 0.8027052 0.7715883 0.7461310 0.7253039 0.7082648 0.6943247 0.6829201
## 
## $新潟県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.4400023 -1.163858
## 43.71429 -0.5224708 -1.265509
## 43.85714 -0.6080083 -1.363614
## 44.00000 -0.6741081 -1.438010
## 44.14286 -0.4205939 -1.189999
## 44.28571 -0.6001038 -1.373170
## 44.42857 -0.4887598 -1.264267
## 44.57143 -0.6974229 -1.491543
## 44.71429 -0.7342089 -1.531330
## 44.85714 -0.7634488 -1.562572
## 45.00000 -0.7868024 -1.587263
## 45.14286 -0.8055301 -1.606885
## 45.28571 -0.8205994 -1.622552
## 45.42857 -0.8327593 -1.635112
## 
## $新潟県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 2.294790 3.018646
## 43.71429 2.284795 3.027834
## 43.85714 2.246739 3.002344
## 44.00000 2.211982 2.975884
## 44.14286 2.486286 3.255691
## 44.28571 2.320609 3.093675
## 44.42857 2.441175 3.216682
## 44.57143 2.302833 3.096953
## 44.71429 2.277386 3.074506
## 44.85714 2.255711 3.054834
## 45.00000 2.237410 3.037871
## 45.14286 2.222060 3.023414
## 45.28571 2.209249 3.011201
## 45.42857 2.198600 3.000952
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.12095822 0.21861199 0.10566355 0.10716019 0.11170882 0.11731220
##  [7] 0.10053111 0.09709017 0.09306076 0.08976421 0.08420553 0.08036355
## [13] 0.07650402 0.07293938
## 
## $富山県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -2.365083 -3.681114
## 43.71429 -2.407277 -3.797339
## 43.85714 -2.680427 -4.155294
## 44.00000 -2.801730 -4.341604
## 44.14286 -2.987368 -4.627920
## 44.28571 -3.094301 -4.794426
## 44.42857 -3.216534 -4.972483
## 44.57143 -3.310007 -5.113615
## 44.71429 -3.400197 -5.249416
## 44.85714 -3.475633 -5.363040
## 45.00000 -3.546147 -5.467940
## 45.14286 -3.607474 -5.559697
## 45.28571 -3.663209 -5.642893
## 45.42857 -3.712773 -5.716808
## 
## $富山県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 2.607000 3.923031
## 43.71429 2.844501 4.234563
## 43.85714 2.891754 4.366621
## 44.00000 3.016050 4.555924
## 44.14286 3.210786 4.851338
## 44.28571 3.328925 5.029050
## 44.42857 3.417596 5.173545
## 44.57143 3.504187 5.307796
## 44.71429 3.586319 5.435537
## 44.85714 3.655161 5.542568
## 45.00000 3.714558 5.636351
## 45.14286 3.768201 5.720424
## 45.28571 3.816217 5.795902
## 45.42857 3.858652 5.862687
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.797904 1.797904 1.797904 1.797904 1.797904 1.797904 1.797904 1.797904
##  [9] 1.797904 1.797904 1.797904 1.797904 1.797904 1.797904
## 
## $石川県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.922508 -3.891976
## 43.71429 -2.131960 -4.212304
## 43.85714 -2.330799 -4.516403
## 44.00000 -2.520493 -4.806515
## 44.14286 -2.702198 -5.084408
## 44.28571 -2.876845 -5.351508
## 44.42857 -3.045198 -5.608982
## 44.57143 -3.207893 -5.857801
## 44.71429 -3.365463 -6.098785
## 44.85714 -3.518366 -6.332629
## 45.00000 -3.666992 -6.559933
## 45.14286 -3.811681 -6.781216
## 45.28571 -3.952731 -6.996934
## 45.42857 -4.090404 -7.207486
## 
## $石川県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 5.518317  7.487784
## 43.71429 5.727768  7.808113
## 43.85714 5.926608  8.112212
## 44.00000 6.116302  8.402323
## 44.14286 6.298006  8.680216
## 44.28571 6.472653  8.947316
## 44.42857 6.641007  9.204790
## 44.57143 6.803701  9.453610
## 44.71429 6.961272  9.694593
## 44.85714 7.114174  9.928438
## 45.00000 7.262800 10.155741
## 45.14286 7.407490 10.377025
## 45.28571 7.548540 10.592743
## 45.42857 7.686212 10.803294
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.4798175 0.7468344 0.7602789 0.7718799 0.7818900 0.7905274 0.7979805
##  [8] 0.8044115 0.8099607 0.8147489 0.8188805 0.8224456 0.8255218 0.8281761
## 
## $福井県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.403936 -2.401134
## 43.71429 -1.224782 -2.268492
## 43.85714 -1.446690 -2.614988
## 44.00000 -1.595171 -2.848212
## 44.14286 -1.697647 -3.010235
## 44.28571 -1.769553 -3.124778
## 44.42857 -1.820461 -3.206580
## 44.57143 -1.856651 -3.265332
## 44.71429 -1.882397 -3.307645
## 44.85714 -1.900675 -3.338134
## 45.00000 -1.913591 -3.360075
## 45.14286 -1.922651 -3.375817
## 45.28571 -1.928936 -3.387058
## 45.42857 -1.933231 -3.395032
## 
## $福井県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 2.363571 3.360769
## 43.71429 2.718450 3.762161
## 43.85714 2.967248 4.135546
## 44.00000 3.138931 4.391972
## 44.14286 3.261427 4.574015
## 44.28571 3.350608 4.705833
## 44.42857 3.416422 4.802541
## 44.57143 3.465474 4.874155
## 44.71429 3.502318 4.927566
## 44.85714 3.530173 4.967632
## 45.00000 3.551352 4.997836
## 45.14286 3.567542 5.020708
## 45.28571 3.579980 5.038101
## 45.42857 3.589584 5.051384
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 2.522326 2.722478 2.271787 2.606117 2.585240 2.631847 2.823122 2.350477
##  [9] 2.453941 2.226539 2.504305 2.683539 2.658612 2.633647
## 
## $山梨県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%          95%
## 43.57143 1.0462598  0.264877436
## 43.71429 1.0924607  0.229581550
## 43.85714 0.5916955 -0.297691046
## 44.00000 0.8987376 -0.005094671
## 44.14286 0.8571599 -0.057630390
## 44.28571 0.8853261 -0.039226095
## 44.42857 1.0590506  0.125207694
## 44.57143 0.5234926 -0.443654421
## 44.71429 0.5931636 -0.391872458
## 44.85714 0.3403312 -0.658167037
## 45.00000 0.5955342 -0.414908162
## 45.14286 0.7533428 -0.268441544
## 45.28571 0.7075344 -0.325303655
## 45.42857 0.6620201 -0.381696192
## 
## $山梨県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 3.998392 4.779775
## 43.71429 4.352496 5.215375
## 43.85714 3.951878 4.841264
## 44.00000 4.313497 5.217330
## 44.14286 4.313320 5.228110
## 44.28571 4.378368 5.302920
## 44.42857 4.587193 5.521036
## 44.57143 4.177461 5.144608
## 44.71429 4.314719 5.299755
## 44.85714 4.112748 5.111246
## 45.00000 4.413077 5.423519
## 45.14286 4.613736 5.635520
## 45.28571 4.609689 5.642527
## 45.42857 4.605274 5.648990
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 7.168342 7.168342 7.168342 7.168342 7.168342 7.168342 7.168342 7.168342
##  [9] 7.168342 7.168342 7.168342 7.168342 7.168342 7.168342
## 
## $長野県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 4.539711 3.1481971
## 43.71429 4.380006 2.9039505
## 43.85714 4.228966 2.6729550
## 44.00000 4.085317 2.4532626
## 44.14286 3.948070 2.2433604
## 44.28571 3.816437 2.0420457
## 44.42857 3.689782 1.8483436
## 44.57143 3.567579 1.6614506
## 44.71429 3.449390 1.4806955
## 44.85714 3.334843 1.3055105
## 45.00000 3.223620 1.1354105
## 45.14286 3.115449 0.9699767
## 45.28571 3.010090 0.8088450
## 45.42857 2.907336 0.6516963
## 
## $長野県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143  9.796973 11.18849
## 43.71429  9.956678 11.43273
## 43.85714 10.107718 11.66373
## 44.00000 10.251367 11.88342
## 44.14286 10.388614 12.09332
## 44.28571 10.520247 12.29464
## 44.42857 10.646902 12.48834
## 44.57143 10.769105 12.67523
## 44.71429 10.887294 12.85599
## 44.85714 11.001841 13.03117
## 45.00000 11.113064 13.20127
## 45.14286 11.221235 13.36671
## 45.28571 11.326594 13.52784
## 45.42857 11.429348 13.68499
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 4.174006 5.953444 5.913918 5.209344 7.274353 8.730583 5.848164 5.974722
##  [9] 6.776509 6.706335 6.355235 7.153760 7.927811 6.334372
## 
## $岐阜県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%        95%
## 43.57143  0.8857855 -0.8548933
## 43.71429  2.1558445  0.1455167
## 43.85714  1.8483268 -0.3038673
## 44.00000  0.9388420 -1.3218251
## 44.14286  2.8217770  0.4647255
## 44.28571  4.1071744  1.6596895
## 44.42857  1.0612772 -1.4727477
## 44.57143  0.7716024 -1.9827627
## 44.71429  1.3024977 -1.5952691
## 44.85714  1.0056816 -2.0120621
## 45.00000  0.4459978 -2.6821633
## 45.14286  1.0459453 -2.1873366
## 45.28571  1.6285747 -1.7060398
## 45.42857 -0.1503569 -3.5831649
## 
## $岐阜県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143  7.462226  9.202905
## 43.71429  9.751044 11.761371
## 43.85714  9.979510 12.131704
## 44.00000  9.479846 11.740513
## 44.14286 11.726929 14.083981
## 44.28571 13.353992 15.801477
## 44.42857 10.635051 13.169076
## 44.57143 11.177842 13.932207
## 44.71429 12.250521 15.148288
## 44.85714 12.406989 15.424733
## 45.00000 12.264472 15.392633
## 45.14286 13.261575 16.494857
## 45.28571 14.227048 17.561663
## 45.42857 12.819101 16.251909
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 20.42803 20.31235 19.41496 21.92611 20.53735 21.41214 20.89974 20.56588
##  [9] 19.09742 21.80839 19.74022 20.97418 21.78941 24.17588
## 
## $静岡県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 16.65236 14.653645
## 43.71429 15.89697 13.559607
## 43.85714 14.56783 12.001915
## 44.00000 16.85605 14.172126
## 44.14286 15.11696 12.247577
## 44.28571 15.77277 12.787466
## 44.42857 14.96189 11.818581
## 44.57143 14.35731 11.070688
## 44.71429 12.59152  9.147505
## 44.85714 15.07132 11.504927
## 45.00000 12.74810  9.046698
## 45.14286 13.76169  9.943621
## 45.28571 14.34223 10.399937
## 45.42857 16.51822 12.464495
## 
## $静岡県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 24.20370 26.20242
## 43.71429 24.72773 27.06509
## 43.85714 24.26209 26.82800
## 44.00000 26.99616 29.68008
## 44.14286 25.95775 28.82713
## 44.28571 27.05151 30.03682
## 44.42857 26.83759 29.98090
## 44.57143 26.77446 30.06108
## 44.71429 25.60331 29.04733
## 44.85714 28.54546 32.11186
## 45.00000 26.73233 30.43374
## 45.14286 28.18668 32.00474
## 45.28571 29.23658 33.17887
## 45.42857 31.83354 35.88726
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1]  92.74995  88.56820 104.85300  91.09712  94.41247 103.28661 108.09240
##  [8] 106.39483 106.99447 106.78266 106.85748 106.83105 106.84038 106.83709
## 
## $愛知県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 78.52791 70.99922
## 43.71429 71.63080 62.66468
## 43.85714 84.67182 73.98856
## 44.00000 68.41848 56.41314
## 44.14286 69.39264 56.14795
## 44.28571 76.15700 61.79545
## 44.42857 78.99565 63.59276
## 44.57143 73.02450 55.35931
## 44.71429 70.69620 51.48105
## 44.85714 67.50381 46.71084
## 45.00000 64.89970 42.68860
## 45.14286 62.32527 38.76534
## 45.28571 59.93491 35.10465
## 45.42857 57.64543 31.60495
## 
## $愛知県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 106.9720 114.5007
## 43.71429 105.5056 114.4717
## 43.85714 125.0342 135.7174
## 44.00000 113.7757 125.7811
## 44.14286 119.4323 132.6770
## 44.28571 130.4162 144.7778
## 44.42857 137.1891 152.5920
## 44.57143 139.7652 157.4304
## 44.71429 143.2927 162.5079
## 44.85714 146.0615 166.8545
## 45.00000 148.8153 171.0264
## 45.14286 151.3368 174.8968
## 45.28571 153.7459 178.5761
## 45.42857 156.0287 182.0692
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 6.105725 8.112670 5.512629 8.117491 6.055762 7.179087 7.164384 6.145271
##  [9] 7.911314 5.822095 7.772570 6.361135 6.974681 7.231946
## 
## $三重県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%        95%
## 43.57143 2.5455515  0.6609094
## 43.71429 4.3155489  2.3054746
## 43.85714 1.4727623 -0.6658142
## 44.00000 3.7642820  1.4598326
## 44.14286 1.5532020 -0.8303095
## 44.28571 2.3535757 -0.2008957
## 44.42857 2.1997151 -0.4284220
## 44.57143 0.9290848 -1.8321972
## 44.71429 2.5145993 -0.3422490
## 44.85714 0.2583436 -2.6869284
## 45.00000 1.9812760 -1.0844496
## 45.14286 0.4470284 -2.6837107
## 45.28571 0.8307238 -2.4216907
## 45.42857 0.9634013 -2.3549656
## 
## $三重県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143  9.665899 11.55054
## 43.71429 11.909790 13.91986
## 43.85714  9.552497 11.69107
## 44.00000 12.470699 14.77515
## 44.14286 10.558323 12.94183
## 44.28571 12.004599 14.55907
## 44.42857 12.129053 14.75719
## 44.57143 11.361456 14.12274
## 44.71429 13.308029 16.16488
## 44.85714 11.385846 14.33112
## 45.00000 13.563863 16.62959
## 45.14286 12.275242 15.40598
## 45.28571 13.118638 16.37105
## 45.42857 13.500490 16.81886
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 6.489463 4.356219 7.218150 5.542532 4.880262 6.542093 5.779253 4.957244
##  [9] 6.283898 5.845662 5.108479 6.078009 5.885812 5.238269
## 
## $滋賀県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%        95%
## 43.57143 2.82271591  0.8816575
## 43.71429 0.59082632 -1.4024519
## 43.85714 2.92465295  0.6518130
## 44.00000 1.19407100 -1.1078653
## 44.14286 0.39403836 -1.9808248
## 44.28571 1.91324824 -0.5371144
## 44.42857 1.08733354 -1.3964185
## 44.57143 0.19351003 -2.3282583
## 44.71429 1.40284031 -1.1810358
## 44.85714 0.90727844 -1.7069437
## 45.00000 0.10611684 -2.5419737
## 45.14286 0.97712784 -1.7231155
## 45.28571 0.72469494 -2.0074353
## 45.42857 0.01725479 -2.7465829
## 
## $滋賀県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143 10.156209 12.09727
## 43.71429  8.121611 10.11489
## 43.85714 11.511647 13.78449
## 44.00000  9.890993 12.19293
## 44.14286  9.366485 11.74135
## 44.28571 11.170939 13.62130
## 44.42857 10.471172 12.95492
## 44.57143  9.720978 12.24275
## 44.71429 11.164957 13.74883
## 44.85714 10.784045 13.39827
## 45.00000 10.110841 12.75893
## 45.14286 11.178890 13.87913
## 45.28571 11.046929 13.77906
## 45.42857 10.459282 13.22312
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 15.37316 14.14761 15.02502 14.96586 14.87713 17.29316 17.45328 16.34117
##  [9] 15.20731 16.04836 15.18526 15.50356 17.21664 16.63220
## 
## $京都府$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 9.038085 5.684500
## 43.71429 7.225258 3.560788
## 43.85714 7.951869 4.207570
## 44.00000 7.745059 3.922599
## 44.14286 7.511644 3.612590
## 44.28571 9.785770 5.811598
## 44.42857 9.806615 5.758718
## 44.57143 8.322876 4.078246
## 44.71429 6.972572 2.613362
## 44.85714 7.645403 3.197148
## 45.00000 6.617400 2.081847
## 45.14286 6.773899 2.152698
## 45.28571 8.328136 3.622844
## 45.42857 7.587631 2.799726
## 
## $京都府$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 21.70823 25.06182
## 43.71429 21.06996 24.73443
## 43.85714 22.09816 25.84246
## 44.00000 22.18665 26.00911
## 44.14286 22.24262 26.14167
## 44.28571 24.80055 28.77472
## 44.42857 25.09994 29.14783
## 44.57143 24.35947 28.60410
## 44.71429 23.44206 27.80127
## 44.85714 24.45131 28.89957
## 45.00000 23.75313 28.28868
## 45.14286 24.23321 28.85442
## 45.28571 26.10515 30.81044
## 45.42857 25.67677 30.46467
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 152.8526 125.4658 185.8152 150.4912 169.3655 191.4329 202.1994 180.1376
##  [9] 154.9577 204.5480 169.3547 188.6405 209.5549 220.3791
## 
## $大阪府$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 130.0614 117.99656
## 43.71429 100.6001  87.43706
## 43.85714 159.0353 144.85879
## 44.00000 121.9249 106.80278
## 44.14286 139.1181 123.10617
## 44.28571 159.5932 142.73829
## 44.42857 168.8433 151.18559
## 44.57143 141.5704 121.15411
## 44.71429 113.9105  92.18140
## 44.85714 161.1624 138.19543
## 45.00000 123.7504  99.60888
## 45.14286 140.9206 115.65914
## 45.28571 159.8092 133.47538
## 45.42857 168.6869 141.32274
## 
## $大阪府$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 175.6437 187.7086
## 43.71429 150.3315 163.4946
## 43.85714 212.5952 226.7717
## 44.00000 179.0575 194.1796
## 44.14286 199.6128 215.6247
## 44.28571 223.2726 240.1275
## 44.42857 235.5556 253.2133
## 44.57143 218.7049 239.1212
## 44.71429 196.0049 217.7340
## 44.85714 247.9337 270.9007
## 45.00000 214.9590 239.1005
## 45.14286 236.3605 261.6219
## 45.28571 259.3006 285.6344
## 45.42857 272.0712 299.4354
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 34.40072 32.97799 50.28616 42.53195 45.99191 48.37530 39.59018 42.56188
##  [9] 42.37184 47.21672 44.40622 45.84386 46.48161 44.10071
## 
## $兵庫県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 25.50449 20.79510
## 43.71429 23.61389 18.65683
## 43.85714 40.47649 35.28356
## 44.00000 32.29608 26.87754
## 44.14286 35.34689 29.71176
## 44.28571 37.33629 31.49259
## 44.42857 28.17077 22.12570
## 44.57143 29.32713 22.32107
## 44.71429 28.49852 21.15443
## 44.85714 32.73296 25.06571
## 45.00000 29.33672 21.35941
## 45.14286 30.21055 21.93478
## 45.28571 30.30413 21.74029
## 45.42857 27.39678 18.55425
## 
## $兵庫県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 43.29696 48.00634
## 43.71429 42.34209 47.29914
## 43.85714 60.09584 65.28876
## 44.00000 52.76782 58.18636
## 44.14286 56.63692 62.27205
## 44.28571 59.41431 65.25800
## 44.42857 51.00960 57.05467
## 44.57143 55.79662 62.80268
## 44.71429 56.24517 63.58926
## 44.85714 61.70049 69.36773
## 45.00000 59.47573 67.45304
## 45.14286 61.47717 69.75295
## 45.28571 62.65909 71.22293
## 45.42857 60.80464 69.64717
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 14.01352 12.13388 12.28820 11.68377 11.67686 11.47710 11.45574 11.38790
##  [9] 11.37438 11.35074 11.34403 11.33560 11.33259 11.32952
## 
## $奈良県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143 10.318010 8.361724
## 43.71429  8.315238 6.293771
## 43.85714  8.014891 5.752740
## 44.00000  7.268914 4.931832
## 44.14286  7.061194 4.617807
## 44.28571  6.731511 4.219349
## 44.42857  6.570954 3.985103
## 44.57143  6.384547 3.735933
## 44.71429  6.252887 3.541731
## 44.85714  6.118662 3.348966
## 45.00000  6.003386 3.176219
## 45.14286  5.890060 3.007363
## 45.28571  5.784184 2.847037
## 45.42857  5.680613 2.690261
## 
## $奈良県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 17.70903 19.66532
## 43.71429 15.95252 17.97399
## 43.85714 16.56150 18.82365
## 44.00000 16.09862 18.43570
## 44.14286 16.29253 18.73592
## 44.28571 16.22268 18.73485
## 44.42857 16.34053 18.92638
## 44.57143 16.39125 19.03986
## 44.71429 16.49588 19.20703
## 44.85714 16.58282 19.35252
## 45.00000 16.68468 19.51184
## 45.14286 16.78115 19.66384
## 45.28571 16.88099 19.81814
## 45.42857 16.97843 19.96879
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 3.067121 2.154932 2.479316 2.293441 2.121242 2.461382 1.416516 1.958519
##  [9] 2.067294 2.067294 2.067294 2.067294 2.067294 2.067294
## 
## $和歌山県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%        95%
## 43.57143  1.19485895  0.2037434
## 43.71429  0.14522832 -0.9186448
## 43.85714  0.40548390 -0.6923363
## 44.00000  0.15740569 -0.9733429
## 44.14286 -0.07523519 -1.2379801
## 44.28571  0.20608121 -0.9878027
## 44.42857 -0.89611220 -2.1203434
## 44.57143 -0.36155042 -1.5897209
## 44.71429 -0.28442472 -1.5293492
## 44.85714 -0.32209532 -1.5869614
## 45.00000 -0.35918116 -1.6436793
## 45.14286 -0.39570866 -1.6995433
## 45.28571 -0.43170230 -1.7545908
## 45.42857 -0.46718482 -1.8088567
## 
## $和歌山県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 4.939383 5.930498
## 43.71429 4.164637 5.228510
## 43.85714 4.553147 5.650968
## 44.00000 4.429476 5.560224
## 44.14286 4.317720 5.480464
## 44.28571 4.716682 5.910566
## 44.42857 3.729143 4.953375
## 44.57143 4.278588 5.506759
## 44.71429 4.419012 5.663936
## 44.85714 4.456683 5.721549
## 45.00000 4.493768 5.778267
## 45.14286 4.530296 5.834131
## 45.28571 4.566290 5.889178
## 45.42857 4.601772 5.943444
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.0466344 0.3212780 0.4430234 0.3012312 0.3067385 0.2762642 0.2728869
##  [8] 0.2656499 0.2638135 0.2619389 0.2612509 0.2607334 0.2605035 0.2603546
## 
## $鳥取県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%        95%
## 43.57143  0.2385792 -0.1891793
## 43.71429 -0.4976920 -0.9312285
## 43.85714 -0.4024311 -0.8499876
## 44.00000 -0.5473291 -0.9965298
## 44.14286 -0.5446027 -0.9952755
## 44.28571 -0.5759178 -1.0270357
## 44.42857 -0.5799089 -1.0313518
## 44.57143 -0.5875035 -1.0391357
## 44.71429 -0.5896322 -1.0414190
## 44.85714 -0.5917478 -1.0436622
## 45.00000 -0.5926574 -1.0446891
## 45.14286 -0.5933835 -1.0455257
## 45.28571 -0.5938161 -1.0460655
## 45.42857 -0.5941639 -1.0465187
## 
## $鳥取県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.854689 2.282448
## 43.71429 1.140248 1.573785
## 43.85714 1.288478 1.736034
## 44.00000 1.149792 1.598992
## 44.14286 1.158080 1.608753
## 44.28571 1.128446 1.579564
## 44.42857 1.125683 1.577126
## 44.57143 1.118803 1.570435
## 44.71429 1.117259 1.569046
## 44.85714 1.115626 1.567540
## 45.00000 1.115159 1.567191
## 45.14286 1.114850 1.566992
## 45.28571 1.114823 1.567072
## 45.42857 1.114873 1.567228
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544
##  [8] 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544 0.4731544
## 
## $島根県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -6.387743 -10.01968
## 43.71429 -6.387743 -10.01968
## 43.85714 -6.387743 -10.01968
## 44.00000 -6.387743 -10.01968
## 44.14286 -6.387743 -10.01968
## 44.28571 -6.387743 -10.01968
## 44.42857 -6.387743 -10.01968
## 44.57143 -6.387743 -10.01968
## 44.71429 -6.387743 -10.01968
## 44.85714 -6.387743 -10.01968
## 45.00000 -6.387743 -10.01968
## 45.14286 -6.387743 -10.01968
## 45.28571 -6.387743 -10.01968
## 45.42857 -6.387743 -10.01968
## 
## $島根県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 7.334052 10.96599
## 43.71429 7.334052 10.96599
## 43.85714 7.334052 10.96599
## 44.00000 7.334052 10.96599
## 44.14286 7.334052 10.96599
## 44.28571 7.334052 10.96599
## 44.42857 7.334052 10.96599
## 44.57143 7.334052 10.96599
## 44.71429 7.334052 10.96599
## 44.85714 7.334052 10.96599
## 45.00000 7.334052 10.96599
## 45.14286 7.334052 10.96599
## 45.28571 7.334052 10.96599
## 45.42857 7.334052 10.96599
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 8.501159 8.347457 8.300098 8.285506 8.281010 8.279625 8.279198 8.279066
##  [9] 8.279026 8.279013 8.279010 8.279008 8.279008 8.279008
## 
## $岡山県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 5.946714 4.594473
## 43.71429 5.517597 4.019559
## 43.85714 5.357723 3.800124
## 44.00000 5.265805 3.667271
## 44.14286 5.194255 3.560226
## 44.28571 5.129666 3.462179
## 44.42857 5.068003 3.368099
## 44.57143 5.007999 3.276400
## 44.71429 4.949228 3.186539
## 44.85714 4.891523 3.098294
## 45.00000 4.834799 3.011545
## 45.14286 4.778999 2.926206
## 45.28571 4.724076 2.842209
## 45.42857 4.669989 2.759490
## 
## $岡山県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 11.05560 12.40785
## 43.71429 11.17732 12.67536
## 43.85714 11.24247 12.80007
## 44.00000 11.30521 12.90374
## 44.14286 11.36776 13.00179
## 44.28571 11.42958 13.09707
## 44.42857 11.49039 13.19030
## 44.57143 11.55013 13.28173
## 44.71429 11.60882 13.37151
## 44.85714 11.66650 13.45973
## 45.00000 11.72322 13.54647
## 45.14286 11.77902 13.63181
## 45.28571 11.83394 13.71581
## 45.42857 11.88803 13.79853
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 3.001437 3.003751 3.002500 3.001536 3.002403 3.002732 3.002203 3.002140
##  [9] 3.002434 3.002405 3.002255 3.002302 3.002372 3.002332
## 
## $広島県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.5492755 -2.428909
## 43.71429 -0.9348414 -3.019806
## 43.85714 -1.1286888 -3.315608
## 44.00000 -1.7379913 -4.246946
## 44.14286 -2.2828492 -5.080693
## 44.28571 -2.5692532 -5.518884
## 44.42857 -2.8774637 -5.989971
## 44.57143 -3.2611251 -6.576697
## 44.71429 -3.5844215 -7.071292
## 44.85714 -3.8571962 -7.488450
## 45.00000 -4.1442022 -7.927308
## 45.14286 -4.4327675 -8.368656
## 45.28571 -4.6954661 -8.770455
## 45.42857 -4.9463804 -9.154175
## 
## $広島県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143  6.552150  8.431784
## 43.71429  6.942343  9.027308
## 43.85714  7.133689  9.320609
## 44.00000  7.741064 10.250018
## 44.14286  8.287655 11.085499
## 44.28571  8.574717 11.524348
## 44.42857  8.881869 11.994376
## 44.57143  9.265404 12.580977
## 44.71429  9.589289 13.076160
## 44.85714  9.862007 13.493260
## 45.00000 10.148712 13.931818
## 45.14286 10.437371 14.373259
## 45.28571 10.700210 14.775199
## 45.42857 10.951045 15.158839
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.901527 1.901527 1.901527 1.901527 1.901527 1.901527 1.901527 1.901527
##  [9] 1.901527 1.901527 1.901527 1.901527 1.901527 1.901527
## 
## $山口県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%        95%
## 43.57143  0.01341885 -0.9860853
## 43.71429 -0.06416424 -1.1047385
## 43.85714 -0.13879939 -1.2188831
## 44.00000 -0.21079908 -1.3289971
## 44.14286 -0.28042423 -1.4354796
## 44.28571 -0.34789534 -1.5386677
## 44.42857 -0.41340076 -1.6388496
## 44.57143 -0.47710291 -1.7362736
## 44.71429 -0.53914298 -1.8311557
## 44.85714 -0.59964466 -1.9236851
## 45.00000 -0.65871700 -2.0140284
## 45.14286 -0.71645678 -2.1023338
## 45.28571 -0.77295029 -2.1887332
## 45.42857 -0.82827491 -2.2733449
## 
## $山口県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 3.789636 4.789140
## 43.71429 3.867219 4.907793
## 43.85714 3.941854 5.021938
## 44.00000 4.013854 5.132052
## 44.14286 4.083479 5.238534
## 44.28571 4.150950 5.341722
## 44.42857 4.216455 5.441904
## 44.57143 4.280157 5.539328
## 44.71429 4.342197 5.634210
## 44.85714 4.402699 5.726740
## 45.00000 4.461771 5.817083
## 45.14286 4.519511 5.905388
## 45.28571 4.576005 5.991788
## 45.42857 4.631329 6.076399
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.18482533 0.52280967 0.66336751 0.51222749 0.52222982 1.06824589
##  [7] 0.06443549 0.48020292 0.51708916 0.45949212 0.52002706 0.51725723
## [13] 0.09645219 0.64037813
## 
## $徳島県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -1.5577794 -2.480259
## 43.71429 -1.3028436 -2.269286
## 43.85714 -1.1813161 -2.157833
## 44.00000 -1.3512920 -2.337780
## 44.14286 -1.3599371 -2.356296
## 44.28571 -0.8323856 -1.838519
## 44.42857 -1.8544828 -2.870297
## 44.57143 -1.5373055 -2.605310
## 44.71429 -1.5352190 -2.621645
## 44.85714 -1.6174116 -2.716858
## 45.00000 -1.5811844 -2.693499
## 45.14286 -1.6079839 -2.733019
## 45.28571 -2.0525500 -3.190163
## 45.42857 -1.5321252 -2.682179
## 
## $徳島県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.927430 2.849909
## 43.71429 2.348463 3.314905
## 43.85714 2.508051 3.484568
## 44.00000 2.375747 3.362235
## 44.14286 2.404397 3.400756
## 44.28571 2.968877 3.975011
## 44.42857 1.983354 2.999168
## 44.57143 2.497711 3.565716
## 44.71429 2.569397 3.655824
## 44.85714 2.536396 3.635842
## 45.00000 2.621239 3.733553
## 45.14286 2.642498 3.767533
## 45.28571 2.245454 3.383067
## 45.42857 2.812882 3.962935
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.5160942 0.3328445 0.3328445 0.3328445 0.3328445 0.3328445 0.3328445
##  [8] 0.3328445 0.3328445 0.3328445 0.3328445 0.3328445 0.3328445 0.3328445
## 
## $香川県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.8458918 -1.566884
## 43.71429 -1.0890135 -1.841700
## 43.85714 -1.0929567 -1.847730
## 44.00000 -1.0968890 -1.853744
## 44.14286 -1.1008106 -1.859742
## 44.28571 -1.1047215 -1.865723
## 44.42857 -1.1086217 -1.871688
## 44.57143 -1.1125114 -1.877637
## 44.71429 -1.1163907 -1.883569
## 44.85714 -1.1202596 -1.889486
## 45.00000 -1.1241183 -1.895388
## 45.14286 -1.1279667 -1.901273
## 45.28571 -1.1318051 -1.907144
## 45.42857 -1.1356334 -1.912999
## 
## $香川県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.878080 2.599072
## 43.71429 1.754703 2.507389
## 43.85714 1.758646 2.513419
## 44.00000 1.762578 2.519433
## 44.14286 1.766500 2.525431
## 44.28571 1.770411 2.531412
## 44.42857 1.774311 2.537377
## 44.57143 1.778200 2.543326
## 44.71429 1.782080 2.549259
## 44.85714 1.785949 2.555176
## 45.00000 1.789807 2.561077
## 45.14286 1.793656 2.566963
## 45.28571 1.797494 2.572833
## 45.42857 1.801322 2.578688
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.3663383 0.2279798 0.4266007 0.2674073 0.4235167 0.3938171 0.4141245
##  [8] 0.3331584 0.4307675 0.3483156 0.4274595 0.3551444 0.4224569 0.3602408
## 
## $愛媛県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.271966 -2.139232
## 43.71429 -1.457246 -2.349350
## 43.85714 -1.286065 -2.192696
## 44.00000 -1.445911 -2.352887
## 44.14286 -1.294460 -2.203902
## 44.28571 -1.326416 -2.237052
## 44.42857 -1.308553 -2.220483
## 44.57143 -1.392540 -2.306070
## 44.71429 -1.299157 -2.214924
## 44.85714 -1.381990 -2.297959
## 45.00000 -1.303892 -2.220415
## 45.14286 -1.376829 -2.293681
## 45.28571 -1.310134 -2.227313
## 45.42857 -1.372852 -2.290295
## 
## $愛媛県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 2.004643 2.871909
## 43.71429 1.913205 2.805310
## 43.85714 2.139267 3.045898
## 44.00000 1.980726 2.887701
## 44.14286 2.141493 3.050935
## 44.28571 2.114050 3.024686
## 44.42857 2.136802 3.048732
## 44.57143 2.058857 2.972386
## 44.71429 2.160692 3.076459
## 44.85714 2.078622 2.994590
## 45.00000 2.158811 3.075334
## 45.14286 2.087118 3.003970
## 45.28571 2.155048 3.072227
## 45.42857 2.093333 3.010777
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.1771545 0.1986951 0.2185795 0.2369352 0.2538795 0.2695211 0.2839601
##  [8] 0.2972890 0.3095931 0.3209511 0.3314359 0.3411146 0.3500492 0.3582968
## 
## $高知県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.540150 -2.449236
## 43.71429 -1.540490 -2.461159
## 43.85714 -1.539036 -2.469461
## 44.00000 -1.536234 -2.474894
## 44.14286 -1.532437 -2.478057
## 44.28571 -1.527923 -2.479433
## 44.42857 -1.522913 -2.479413
## 44.57143 -1.517579 -2.478313
## 44.71429 -1.512061 -2.476386
## 44.85714 -1.506465 -2.473841
## 45.00000 -1.500877 -2.470844
## 45.14286 -1.495360 -2.467531
## 45.28571 -1.489964 -2.464009
## 45.42857 -1.484727 -2.460365
## 
## $高知県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.894459 2.803545
## 43.71429 1.937880 2.858549
## 43.85714 1.976195 2.906620
## 44.00000 2.010105 2.948764
## 44.14286 2.040197 2.985816
## 44.28571 2.066966 3.018475
## 44.42857 2.090833 3.047334
## 44.57143 2.112157 3.072891
## 44.71429 2.131247 3.095573
## 44.85714 2.148368 3.115744
## 45.00000 2.163749 3.133716
## 45.14286 2.177589 3.149760
## 45.28571 2.190063 3.164107
## 45.42857 2.201321 3.176959
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1]  6.931566  5.725743  5.629458  8.628662  8.983534 12.031477  9.483682
##  [8]  7.627112  6.645952  6.544956  8.471871  9.288656 10.936533  9.357903
## 
## $福岡県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143  -6.691442 -13.90303
## 43.71429 -10.275570 -18.74615
## 43.85714 -11.670472 -20.82850
## 44.00000  -9.382699 -18.91734
## 44.14286  -9.643025 -19.50333
## 44.28571  -7.484750 -17.81602
## 44.42857 -11.375240 -22.41729
## 44.57143 -15.847055 -28.27353
## 44.71429 -18.681077 -32.08840
## 44.85714 -20.021544 -34.08500
## 45.00000 -18.902588 -33.39375
## 45.14286 -18.783435 -33.64390
## 45.28571 -18.001197 -33.31991
## 45.42857 -20.757381 -36.69945
## 
## $福岡県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 20.55457 27.76616
## 43.71429 21.72706 30.19764
## 43.85714 22.92939 32.08742
## 44.00000 26.64002 36.17466
## 44.14286 27.61009 37.47040
## 44.28571 31.54770 41.87897
## 44.42857 30.34260 41.38465
## 44.57143 31.10128 43.52775
## 44.71429 31.97298 45.38030
## 44.85714 33.11146 47.17491
## 45.00000 35.84633 50.33749
## 45.14286 37.36075 52.22121
## 45.28571 39.87426 55.19297
## 45.42857 39.47319 55.41526
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.402804 1.402804 1.402804 1.402804 1.402804 1.402804 1.402804 1.402804
##  [9] 1.402804 1.402804 1.402804 1.402804 1.402804 1.402804
## 
## $佐賀県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.5667092 -1.609306
## 43.71429 -0.6464388 -1.731242
## 43.85714 -0.7231804 -1.848608
## 44.00000 -0.7972468 -1.961883
## 44.14286 -0.8688995 -2.071467
## 44.28571 -0.9383604 -2.177698
## 44.42857 -1.0058189 -2.280867
## 44.57143 -1.0714389 -2.381224
## 44.71429 -1.1353630 -2.478987
## 44.85714 -1.1977162 -2.574348
## 45.00000 -1.2586090 -2.667476
## 45.14286 -1.3181394 -2.758520
## 45.28571 -1.3763949 -2.847614
## 45.42857 -1.4334542 -2.934878
## 
## $佐賀県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 3.372317 4.414914
## 43.71429 3.452046 4.536850
## 43.85714 3.528788 4.654216
## 44.00000 3.602854 4.767491
## 44.14286 3.674507 4.877074
## 44.28571 3.743968 4.983306
## 44.42857 3.811427 5.086475
## 44.57143 3.877047 5.186832
## 44.71429 3.940971 5.284595
## 44.85714 4.003324 5.379956
## 45.00000 4.064217 5.473084
## 45.14286 4.123747 5.564127
## 45.28571 4.182003 5.653222
## 45.42857 4.239062 5.740486
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 1.3895945 0.5504742 0.9831559 0.7599363 1.0682992 1.3531415 0.8917590
##  [8] 1.0929263 0.9426595 1.0201251 0.9801420 1.1248633 1.3550959 0.9658518
## 
## $長崎県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143 -0.7648639 -1.905365
## 43.71429 -1.8433184 -3.110516
## 43.85714 -1.9052550 -3.434287
## 44.00000 -2.4161528 -4.097473
## 44.14286 -2.4328482 -4.286244
## 44.28571 -2.4155572 -4.410586
## 44.42857 -3.1409656 -5.275761
## 44.57143 -3.3319240 -5.674298
## 44.71429 -3.7701358 -6.264939
## 44.85714 -3.9983522 -6.654974
## 45.00000 -4.3094690 -7.109620
## 45.14286 -4.4309343 -7.371996
## 45.28571 -4.4506029 -7.523954
## 45.42857 -5.0814547 -8.282705
## 
## $長崎県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%       95%
## 43.57143 3.544053  4.684554
## 43.71429 2.944267  4.211464
## 43.85714 3.871567  5.400599
## 44.00000 3.936025  5.617345
## 44.14286 4.569447  6.422842
## 44.28571 5.121840  7.116869
## 44.42857 4.924484  7.059279
## 44.57143 5.517777  7.860151
## 44.71429 5.655455  8.150258
## 44.85714 6.038602  8.695224
## 45.00000 6.269753  9.069904
## 45.14286 6.680661  9.621722
## 45.28571 7.160795 10.234146
## 45.42857 7.013158 10.214409
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 5.113304 6.940396 6.375117 6.375117 6.375117 6.375117 6.375117 6.375117
##  [9] 6.375117 6.375117 6.375117 6.375117 6.375117 6.375117
## 
## $熊本県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                  80%       95%
## 43.57143  0.90983623 -1.315345
## 43.71429  1.32938125 -1.640910
## 43.85714  0.60968934 -2.442344
## 44.00000  0.27120804 -2.960006
## 44.14286 -0.04946494 -3.450433
## 44.28571 -0.35487569 -3.917519
## 44.42857 -0.64701587 -4.364309
## 44.57143 -0.92747833 -4.793239
## 44.71429 -1.19756067 -5.206294
## 44.85714 -1.45833662 -5.605117
## 45.00000 -1.71070665 -5.991083
## 45.14286 -1.95543476 -6.365363
## 45.28571 -2.19317578 -6.728956
## 45.42857 -2.42449604 -7.082730
## 
## $熊本県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%      95%
## 43.57143  9.316772 11.54195
## 43.71429 12.551410 15.52170
## 43.85714 12.140545 15.19258
## 44.00000 12.479026 15.71024
## 44.14286 12.799699 16.20067
## 44.28571 13.105110 16.66775
## 44.42857 13.397250 17.11454
## 44.57143 13.677713 17.54347
## 44.71429 13.947795 17.95653
## 44.85714 14.208571 18.35535
## 45.00000 14.460941 18.74132
## 45.14286 14.705669 19.11560
## 45.28571 14.943410 19.47919
## 45.42857 15.174730 19.83296
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1]  0.22159923  0.04290662 -0.05262154  0.06482064  0.06266513  0.06058129
##  [7]  0.05856675  0.05661919  0.05473640  0.05291622  0.05115657  0.04945543
## [13]  0.04781086  0.04622098
## 
## $大分県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -1.164372 -1.898061
## 43.71429 -1.560541 -2.409356
## 43.85714 -1.832342 -2.774469
## 44.00000 -1.784579 -2.763593
## 44.14286 -1.798858 -2.784289
## 44.28571 -1.812201 -2.803592
## 44.42857 -1.824677 -2.821606
## 44.57143 -1.836350 -2.838427
## 44.71429 -1.847277 -2.854142
## 44.85714 -1.857511 -2.868831
## 45.00000 -1.867102 -2.882566
## 45.14286 -1.876092 -2.895416
## 45.28571 -1.884525 -2.907442
## 45.42857 -1.892437 -2.918701
## 
## $大分県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 1.607571 2.341260
## 43.71429 1.646355 2.495169
## 43.85714 1.727099 2.669225
## 44.00000 1.914221 2.893234
## 44.14286 1.924188 2.909619
## 44.28571 1.933363 2.924754
## 44.42857 1.941811 2.938740
## 44.57143 1.949588 2.951666
## 44.71429 1.956750 2.963615
## 44.85714 1.963344 2.974663
## 45.00000 1.969415 2.984879
## 45.14286 1.975003 2.994327
## 45.28571 1.980147 3.003063
## 45.42857 1.984879 3.011143
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 0.3782929 0.1467928 0.1467928 0.1467928 0.1467928 0.1467928 0.1467928
##  [8] 0.1467928 0.1467928 0.1467928 0.1467928 0.1467928 0.1467928 0.1467928
## 
## $宮崎県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%       95%
## 43.57143 -2.343113 -3.783739
## 43.71429 -2.690929 -4.193129
## 43.85714 -3.019929 -4.696291
## 44.00000 -3.317827 -5.151886
## 44.14286 -3.592064 -5.571296
## 44.28571 -3.847518 -5.961978
## 44.42857 -4.087588 -6.329133
## 44.57143 -4.314759 -6.676561
## 44.71429 -4.530910 -7.007136
## 44.85714 -4.737505 -7.323096
## 45.00000 -4.935709 -7.626223
## 45.14286 -5.126469 -7.917965
## 45.28571 -5.310565 -8.199515
## 45.42857 -5.488650 -8.471873
## 
## $宮崎県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 3.099699 4.540325
## 43.71429 2.984515 4.486714
## 43.85714 3.313515 4.989876
## 44.00000 3.611413 5.445472
## 44.14286 3.885650 5.864882
## 44.28571 4.141103 6.255564
## 44.42857 4.381173 6.622719
## 44.57143 4.608344 6.970147
## 44.71429 4.824496 7.300722
## 44.85714 5.031091 7.616682
## 45.00000 5.229295 7.919809
## 45.14286 5.420055 8.211551
## 45.28571 5.604150 8.493101
## 45.42857 5.782236 8.765459
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 4.292481 3.859387 3.594277 3.431995 3.332657 3.271850 3.234628 3.211843
##  [9] 3.197895 3.189358 3.184132 3.180933 3.178975 3.177776
## 
## $鹿児島県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                 80%       95%
## 43.57143  0.3379122 -1.755510
## 43.71429 -0.8406932 -3.328765
## 43.85714 -1.3951053 -4.036325
## 44.00000 -1.6869190 -4.396708
## 44.14286 -1.8514926 -4.595816
## 44.28571 -1.9493906 -4.713348
## 44.42857 -2.0104678 -4.787054
## 44.57143 -2.0504640 -4.836161
## 44.71429 -2.0780928 -4.871032
## 44.85714 -2.0983480 -4.897490
## 45.00000 -2.1141601 -4.918906
## 45.14286 -2.1272755 -4.937271
## 45.28571 -2.1387454 -4.953776
## 45.42857 -2.1492065 -4.969141
## 
## $鹿児島県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 8.247049 10.34047
## 43.71429 8.559467 11.04754
## 43.85714 8.583659 11.22488
## 44.00000 8.550909 11.26070
## 44.14286 8.516807 11.26113
## 44.28571 8.493090 11.25705
## 44.42857 8.479723 11.25631
## 44.57143 8.474149 11.25985
## 44.71429 8.473884 11.26682
## 44.85714 8.477064 11.27621
## 45.00000 8.482424 11.28717
## 45.14286 8.489141 11.29914
## 45.28571 8.496694 11.31173
## 45.42857 8.504758 11.32469
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##  [1] 26.08975 23.00863 24.61510 23.72036 26.08172 25.07092 27.34335 26.07694
##  [9] 26.07694 26.07694 26.07694 26.07694 26.07694 26.07694
## 
## $沖縄県$lower
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##                80%         95%
## 43.57143 14.347703   8.1318409
## 43.71429 10.167971   3.3705386
## 43.85714 10.762694   3.4296711
## 44.00000  8.925226   1.0931525
## 44.14286 10.400431   2.0992554
## 44.28571  8.550943  -0.1942076
## 44.42857 10.025250   0.8576006
## 44.57143  7.025431  -3.0598284
## 44.71429  5.975709  -4.6652392
## 44.85714  4.978149  -6.1908751
## 45.00000  4.025671  -7.6475644
## 45.14286  3.112665  -9.0438873
## 45.28571  2.234595 -10.3867792
## 45.42857  1.387734 -11.6819414
## 
## $沖縄県$upper
## Time Series:
## Start = c(43, 5) 
## End = c(45, 4) 
## Frequency = 7 
##               80%      95%
## 43.57143 37.83179 44.04765
## 43.71429 35.84928 42.64671
## 43.85714 38.46751 45.80054
## 44.00000 38.51550 46.34758
## 44.14286 41.76302 50.06419
## 44.28571 41.59091 50.33606
## 44.42857 44.66145 53.82910
## 44.57143 45.12845 55.21371
## 44.71429 46.17817 56.81912
## 44.85714 47.17573 58.34475
## 45.00000 48.12821 59.80144
## 45.14286 49.04121 61.19776
## 45.28571 49.91928 62.54066
## 45.42857 50.76614 63.83582